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Audio information plays an important role in the growing digital content that is available nowadays, in this manner, this leads to an outcome that shows the necessity of developing systems or applications that automatically analyze this content. Some of the most common applications in this area are: recognition of audio events for home automation and automatic surveillance systems, voice recognition, music information retrieval, multimodal analysis, recognition of human activities, among others in the area of Environmental Intelligence and Artificial Intelligence. Nevertheless, studies in some of the areas mentioned above are scarce, mainly in the area of recognition of activities through environmental sound. It is therefore, in this paper, an evaluation and comparison of the performance of the Support Vector Machines (SVM), k Nearest Neighbors (kNN), Random forests (RF), Extra trees (ET) and Gradient boosting (GB) classifiers applied to the recognition of activities carried out by infants in the age range of 12 to 36 months is made.

Comparación del nivel de precisión de los clasificadores Support Vector Machines, k Nearest Neighbors, Random Forests, Extra Trees y Gradient Boosting en el reconocimiento de actividades infantiles utilizando sonido ambiental